Model-based dynamic position correction for digital lithography tools

Abstract

To solve the problem in a photolithography system including a number of heat sources and components having different conductivity and heat capacity, each of which can contribute to variations causing non-uniform patterning and adversely affects the repeatability of total pitch and overlay correction.SOLUTION: The present disclosure relates generally to photolithography systems and methods for correcting position errors in photolithography systems. When a photolithography system is first started, the system enters a stabilization period. During the stabilization period, data such as position readings, temperature, pressure, and humidity data is collected when the system is printing or exposing substrates. Based on the collected data and position readings, a model is created. The model is then used to estimate an error in a subsequent stabilization period, and the estimated error is dynamically corrected during the subsequent stabilization period.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] TECHNICAL FIELD

[0001] Embodiments of the present disclosure generally relate to photolithography systems and methods for correcting position errors in photolithography systems. [Background technology]

[0002] Photolithography is widely used in the fabrication of semiconductor devices and display devices such as liquid crystal displays (LCDs). Large-area substrates are often used in the fabrication of LCDs. LCDs, or flat panels, are commonly used in active matrix displays such as those found in computers, touchscreen devices, personal digital assistants (PDAs), mobile phones, and television monitors. Generally, flat panels may include a layer of liquid crystal material that forms pixels sandwiched between two plates. When power from a power source is applied across the liquid crystal material, the amount of light that passes through the liquid crystal material is controlled at the pixel locations, allowing an image to be generated.

[0003]

[0003] Microlithography techniques are commonly used to create electrical features that are incorporated as part of the liquid crystal material layer that forms the pixels. According to this technique, a light-sensitive photoresist is typically applied to at least one surface of a substrate. A pattern generator then exposes selected areas of the light-sensitive photoresist to light as part of a pattern, causing chemical changes in the photoresist in the selected areas that prepare them for subsequent material removal and / or material addition processes to create the electrical features.

[0004] However, tools used in such microlithography techniques can take eight hours or more to sufficiently stabilize their printing and patterning behavior, during which time the patterning of the photoresist can become non-uniform due to various influences such as thermal fluctuations. The tools contain numerous heat sources and components with different conductivities and heat capacities, each of which can contribute to variations that cause non-uniform patterning and adversely affect the repeatability of total pitch and overlay correction.

[0005]

[0005] To continue providing consumers with display devices and other devices at prices they demand, new apparatus, approaches, and systems are needed to accurately and cost-effectively produce patterns on substrates, such as large area substrates. Summary of the Invention

[0006]

[0006] The present disclosure generally relates to photolithography systems and methods for correcting position errors in photolithography systems. When a photolithography system is first started, the system undergoes a stabilization period. During the stabilization period, position readings and data such as temperature, barometric pressure, and humidity data are collected while the system is printing or exposing a substrate. A model is created based on the collected data and position readings. The model is then used to estimate errors in subsequent stabilization periods, and the estimated errors are dynamically corrected during the subsequent stabilization periods.

[0007]

[0007] In one embodiment, a method includes starting a photolithography system and entering a stabilization period, collecting data and position readings while the photolithography system is printing during the stabilization period, creating a model based on the data and position readings, and using the model to dynamically correct estimation errors during a subsequent stabilization period.

[0008] In another embodiment, a method includes starting a photolithography system and entering a stabilization period and collecting temperature data and position readings while the photolithography system is printing during the stabilization period. The temperature data is collected during heating and cooling periods. The method further includes creating a model based on the temperature data and position readings, calibrating the model, using the calibrated model to estimate an error in a subsequent stabilization period, and dynamically correcting the estimated error during the subsequent stabilization period.

[0009]

[0009] In yet another embodiment, a method includes starting a photolithography system and entering a stabilization period, collecting temperature data and position readings while the photolithography system is printing during the stabilization period, creating a model based on the temperature data and position readings, forming an optimization problem to determine the thermal capacity and conductivity of the photolithography system, using the model and the optimization problem to estimate errors in subsequent stabilization periods, and dynamically correcting the estimated errors during the subsequent stabilization periods.

[0010]

[0010] So that the above-mentioned features of the present disclosure may be understood in detail, a more particular description of the present disclosure briefly summarized above may be had by reference to embodiments, some of which are illustrated in the accompanying drawings. It should be noted, however, that the accompanying drawings illustrate only exemplary embodiments and therefore should not be considered as limiting the scope thereof, as other equally effective embodiments may be recognized. [Brief explanation of the drawings]

[0011] [Figure 1A] FIG. 1 is a perspective view of a photolithography system, according to one embodiment. [Figure 1B] FIG. 2 is a perspective view of a photolithography system according to another embodiment. [Figure 2]1 is a perspective schematic diagram of an image projection device according to an embodiment disclosed herein; [Figure 3] 1 illustrates a method for modeling and calibrating system behavior to estimate position perturbations that occur during a stabilization period, according to embodiments disclosed herein. [Figure 4A] 10 illustrates an exemplary graph of data measurements according to embodiments disclosed herein. [Figure 4B] 10 illustrates an exemplary graph of data measurements according to embodiments disclosed herein. [Figure 4C] 10 illustrates an exemplary graph of data measurements according to embodiments disclosed herein. [Figure 4D] 10 illustrates an exemplary graph of data measurements according to embodiments disclosed herein. [Figure 4E] 10 illustrates an exemplary graph of data measurements according to embodiments disclosed herein. [Figure 4F] 10 illustrates an exemplary graph of data measurements according to embodiments disclosed herein. [Figure 5] 1 illustrates an alignment configuration of a first bridge component and a second bridge component, each having multiple eyes disposed thereon, according to embodiments disclosed herein. [Figure 6A] 10 shows an exemplary graph of data measurements and position readings at a stage velocity of 200 mm / sec, according to embodiments disclosed herein. [Figure 6B] 10 shows an exemplary graph of data measurements and position readings at a stage velocity of 200 mm / sec, according to embodiments disclosed herein. [Figure 6C] 10 shows an exemplary graph of data measurements and position readings at a stage velocity of 200 mm / sec, according to embodiments disclosed herein. [Figure 7A] 10 shows an exemplary graph of data measurements and position readings at a stage velocity of 100 mm / sec, according to embodiments disclosed herein. [Figure 7B]10 shows an exemplary graph of data measurements and position readings at a stage velocity of 100 mm / sec, according to embodiments disclosed herein. [Figure 7C] 10 shows an exemplary graph of data measurements and position readings at a stage velocity of 100 mm / sec, according to embodiments disclosed herein. DETAILED DESCRIPTION OF THE INVENTION

[0012]

[0019] For ease of understanding, wherever possible, identical reference numerals have been used to designate identical elements common to the figures. It is contemplated that elements and features of one embodiment may be beneficially incorporated in other embodiments without further recitation.

[0013]

[0020] The present disclosure generally relates to photolithography systems and methods for correcting position errors in photolithography systems. When a photolithography system is first started, the system undergoes a stabilization period. During the stabilization period, position readings and data such as temperature, barometric pressure, and humidity data are collected while the system is printing or exposing a substrate. A model is created based on the collected data and position readings. The model is then used to estimate errors in subsequent stabilization periods, and the estimated errors are dynamically corrected during the subsequent stabilization periods.

[0014]

[0021] FIG. 1A is a perspective view of a photolithography system 100 according to an embodiment disclosed herein. The system 100 includes a base frame 110, a slab 120, a stage 130, and a processing unit 160. The base frame 110 rests on the floor of a fabrication facility and supports the slab 120. A passive air isolator 112 is disposed between the base frame 110 and the slab 120. In one embodiment, the slab 120 is a monolithic piece of granite, and the stage 130 is disposed on the slab 120. A substrate 140 is supported by the stage 130. Multiple holes (not shown) are formed in the stage 130 through which multiple lift pins (not shown) can extend. In some embodiments, the lift pins rise to an extended position to receive the substrate 140, for example, from one or more transfer robots (not shown). The one or more transfer robots are used to load and unload the substrate 140 from the stage 130.

[0015]

[0022] Substrate 140 may comprise any suitable material used as part of a flat panel display, such as an alkaline earth boroaluminosilicate. In other embodiments, substrate 140 may be made of other materials. In some embodiments, substrate 140 may have a photoresist layer formed thereon. Photoresists are highly radiation-sensitive. Positive photoresists include portions of a photoresist that, upon exposure to radiation, become soluble in a photoresist developer applied to the photoresist after a pattern has been written into the photoresist. Negative photoresists include portions of a photoresist that, upon exposure to radiation, become insoluble in a photoresist developer applied to the photoresist after a pattern has been written into the photoresist. The chemical composition of the photoresist determines whether the photoresist is a positive or negative photoresist. Examples of photoresists include, but are not limited to, at least one of diazonaphthoquinone, phenol formaldehyde resin, poly(methyl methacrylate), poly(methyl glutarimide), and SU-8. In this way, patterns are created on the surface of the substrate 140 to form electronic circuits.

[0016]

[0023] The system 100 includes a pair of supports 122 and a pair of tracks 124. The pair of supports 122 is disposed on a slab 120; in one embodiment, the slab 120 and the pair of supports 122 are a single piece of material. The pair of tracks 124 are supported by the pair of supports 122, and the stage 130 moves along the tracks 124 in the x-direction. In one embodiment, the pair of tracks 124 are a pair of parallel magnetic channels. As shown, each track 124 of the pair of tracks 124 is linear. In another embodiment, air bearings are utilized for high-precision, non-contact motion, and linear motors are configured to provide force to move the stage 130 back and forth in the x- and y-directions. In other embodiments, one or more of the tracks 124 are non-linear. An encoder 126 is coupled to the stage 130 to provide position information to a controller (not shown).

[0017]

[0024] Processing apparatus 160 includes a support 162 and processing units 164. Support 162 is positioned on slab 120 and includes an opening 166 through which stage 130 passes under processing units 164. Processing units 164 are supported by support 162. In one embodiment, processing units 164 are pattern generators configured to expose photoresist in a photolithography process. In some embodiments, the pattern generator is configured to perform a maskless lithography process. Processing units 164 include multiple image projection devices (shown in FIG. 2). In one embodiment, processing units 164 include as many as 84 image projection devices. Each image projection device is disposed within a case 165. Processing apparatus 160 is useful for performing maskless direct patterning.

[0018]

[0025] During operation, the stage 130 moves in the x-direction from a loading position, as shown in FIG. 1A, to a processing position. The processing position is one or more positions of the stage 130 as it passes under the processing unit 164. During operation, the stage 130 is lifted by a plurality of air bearings (not shown) and moves from the loading position to the processing position along a pair of tracks 124. A plurality of vertical guide air bearings (not shown) are coupled to the stage 130 and positioned adjacent the inner wall 128 of each support 122 to stabilize the movement of the stage 130. The stage 130 also moves in the y-direction by moving along the tracks 150 to process and / or index the substrate 140. The stage 130 is capable of independent movement, scanning the substrate 140 in one direction and stepping it in the other direction.

[0019]

[0026] A metrology system measures the X and Y lateral position coordinates of each of the stages 130 in real time so that each of the multiple image projection devices can accurately identify the position of the pattern being written on the photoresist-covered substrate. The metrology system also provides real-time measurements of the angular position of each of the stages 130 about the vertical, or z-axis. The angular position measurements can be used to keep the angular position constant during a scan by a servo mechanism, or can be used to apply corrections to the position of the pattern being written on the substrate 140 by the image projection device 270 as shown in FIG. 2. These techniques can be used in combination.

[0020]

[0027] 1B is a perspective view of a photolithography system 190 according to an embodiment disclosed herein. System 190 is similar to system 100. However, system 190 includes two stages 130. Each of the two stages 130 is capable of independent movement, scanning a substrate 140 in one direction and stepping it in the other direction. In some embodiments, while one of the two stages 130 is scanning a substrate 140, the other of the two stages 130 is unloading the exposed substrate and loading the next substrate to be exposed.

[0021]

[0028] 1A-1B show two embodiments of a photolithography system, other systems and configurations are also contemplated herein, for example, photolithography systems including any suitable number of stages are also contemplated.

[0022]

[0029] 2 is a perspective schematic diagram of an image projection device 270 according to one embodiment that is useful in a photolithography system such as system 100 or system 190. Image projection device 270 includes one or more spatial light modulators 280, an alignment and inspection system 284 including a focus sensor 283 and a camera 285, and projection optics 286. The components of the image projection device vary depending on the spatial light modulator being used. Spatial light modulators include, but are not limited to, micro LEDs, digital micromirror devices (DMDs), liquid crystal displays (LCDs), and vertical cavity surface emitting lasers (VCSELs).

[0023]

[0030] In operation, spatial light modulator 280 is used to modulate one or more properties, such as amplitude, phase, or polarization, of light that is projected through image projection device 270 onto a substrate, such as substrate 140. Alignment and inspection system 284 is used for aligning and inspecting the components of image projection device 270. In one embodiment, focus sensor 283 includes multiple lasers that are directed through the lens of camera 285 and back through the lens of camera 285 and imaged onto a sensor to detect whether image projection device 270 is in focus. Camera 285 is used to image a substrate, such as substrate 140, to ensure that the alignment of image projection device 270 and photolithography system 100 or 190 is correct or within a predetermined tolerance. Projection optics 286, such as one or more lenses, is used to project light onto a substrate, such as substrate 140.

[0024]

[0031] When the photolithography system 100, 190 is first started, the system 100, 190 undergoes a stabilization period. The stabilization period is the time it takes for the printing and patterning behavior of the system to stabilize (i.e., the time it takes for the system to fully warm up). During the stabilization period of the photolithography system 100, 190, various influences and variations, such as thermal fluctuations, occur, which can adversely affect the repeatability of total pitch and overlay correction. In some cases, due to various influences and variations, it can take eight hours or more for the photolithography system 100, 190 to stabilize its printing and patterning behavior. Furthermore, each system 100, 190 contains numerous heat sources and components with different conductivities and heat capacities, each of which can contribute to variations and make it difficult to closely monitor the system 100, 190.

[0025]

[0032] To expose substrates with precision and accuracy using the system 100, 190 during the stabilization period, model-based software corrections can be utilized to correct for errors that occur during the stabilization period. The behavior of the system 100, 190 can be modeled and calibrated to estimate potential variations that occur during the stabilization period, as described below in FIG. 3, to enhance the repeatability of total pitch and overlay corrections. The model can then be used to correct overlay and total pitch errors during subsequent stabilization periods of the system 100, 190. By utilizing this model for dynamic position correction, costly hardware solutions can be eliminated or reduced. Furthermore, the model can be used for dynamic position correction because the position corrections are applied to a digital mask.

[0026]

[0033] 3 illustrates a method 300 for modeling and calibrating system behavior to estimate position perturbations that occur during stabilization, according to embodiments disclosed herein. Method 300 can be utilized in photolithography systems 100 and 190 of FIGS. 1A and 1B, respectively.

[0027]

[0034] Method 300 begins at step 302, where the photolithography system is started and enters a stabilization period. During the stabilization period, the printing and patterning behavior of the system may become unstable due to various influences and fluctuations, such as fluctuations in heat, air pressure, and / or humidity. The stabilization period is the time it takes for the printing and patterning behavior of the system to stabilize (i.e., the time it takes for the system to fully warm up).

[0028]

[0035] In step 304, data and position readings are collected while the photolithography system is printing or exposing a substrate during a stabilization period. Data is collected continuously as the system is aligning and exposing a substrate to mimic a production line. In one embodiment, the collected data is temperature data. The temperature data can be collected using one or more temperature sensors placed near parts of the tool that are known to fluctuate in temperature during heating and cooling, such as encoders. For example, approximately 20 temperature sensors can be placed on the photolithography tool to collect and monitor temperatures of chucks, encoders, bridges / risers, etc.

[0029]

[0036] To collect position readings, a calibration plate or alignment marks on the substrate (shown in Figure 5) can be captured periodically throughout the stabilization period. The calibration plate can be used as a reference during the stabilization period. Additionally or alternatively, the change in encoder counts relative to the interferometer readings used as the reference may be recorded at further intervals. The position change relative to the reference used is then recorded.

[0030]

[0037] The pattern printing position on the substrate or calibration plate can be unintentionally disturbed due to thermal effects and fluctuations that occur during the stabilization period. Therefore, perturbations in position readings on the substrate or calibration plate can be directly related to temperature fluctuations. Other effects, such as barometric pressure, humidity, etc., can also cause perturbations in position readings. In such cases, sensors configured to collect barometric pressure data, humidity data, etc., can be utilized instead of or in addition to a temperature sensor. However, thermal effects will be used as an example throughout.

[0031]

[0038] 4A-4F show example graphs of data measurements and position readings. Figures 4A-4F are merely examples of data measurements and are not intended to be limiting. Figure 4A shows the temperature change in degrees Celsius over time for the bridge and riser components in the system at a stage speed of 200 mm / s. Figure 4B shows the corresponding position marks along the y-axis in micrometers detected during heating of the bridge and riser components at a stage speed of 200 mm / s, which further illustrates position perturbations due to thermal effects. Figure 4C shows the temperature change in degrees Celsius over time for the first bridge component, the second bridge component, and the riser components in the system at a stage speed of 100 mm / s. Figure 4D shows the corresponding position marks along the y-axis in micrometers detected during heating of the bridge and riser components at a stage speed of 100 mm / s, which further illustrates position perturbations in the y-axis due to thermal effects. Figure 4E shows temperature readings in degrees Celsius over a period of time for the master and slave motors that move the stages in a photolithography system. Figure 4F shows position marks detected during the cooling period along the x- and y-axes over a period of time. Figures 4A-4F demonstrate that the behavior of the system during stabilization can be mathematically described.

[0032]

[0039] FIG. 5 illustrates an alignment configuration 500 for a first bridge component 504 and a second bridge component 506, each having multiple eyes 508 disposed thereon, according to one embodiment. The alignment configuration 500 can be used to collect data for the graphs shown in FIGS. 4A-4F above and for the graphs shown in FIGS. 6A-6C and 7A-7C below. The first bridge component 504 and the second bridge component 506 are disposed above a substrate or plate 502. The plate 502 includes multiple alignment marks 510. While 32 alignment marks 510 are shown, any number of alignment marks can be utilized. Furthermore, although two bridge components 504, 506 are shown, additional bridge components may be utilized in the photolithography system, and each bridge component 504, 506 may have more than four eyes disposed thereon. The alignment configuration 500 may include an exposure unit having a camera (not shown) utilized to collect position readings.

[0033]

[0040] 6A-6C show exemplary graphs of data measurements and position readings at a stage speed of 200 mm / sec. 7A-7C show exemplary graphs of data measurements and position readings at a stage speed of 100 mm / sec. 6A-6C and 7A-7C are merely examples of data measurements and are not intended to be limiting. The temperature and position data displayed in the graphs of FIGS. 6A-6C and 7A-7C can be collected or measured using any number of temperature sensors and any number of position marks located on the plate.

[0034]

[0041] 6A and 7A show position marks detected along the x-axis in micrometers over a period of time in hours during the stabilization period, illustrating x-axis position perturbations due to thermal effects. 6B and 7B show temperatures measured in degrees Celsius at two different locations on the chuck of the system over a period of time in hours during the stabilization period. 6C and 7C show temperatures in degrees Celsius of the first, second, and third encoders of the system over a period of time in hours during the stabilization period.

[0035]

[0042] In step 306, a model is created based on the collected data and position readings. The model may include more than one subset of the data, such as a model created to account for temperature effects, barometric pressure effects, and / or humidity effects. When creating the model, the system is assumed to be linear or weakly nonlinear. The model may use effective heat capacity and transfer rate as model parameters. The model may further take into account that the system operates in an iterative manner. Data graphed in one or more of Figures 4A-4F, 6A-6C, and 7A-7C may be used independently or in combination to aid in creating the model.

[0036]

[0043] Additionally, dynamic eye-to-eye and / or bridge-to-bridge models can be incorporated into the created model. The model can capture variations in eye centers relative to one another or drift in separation between bridges, such as first and second bridge components 504, 506 and eye 508 in FIG. 5. The dynamic eye-to-eye and / or bridge-to-bridge models can be empirical models, and the model parameters can be calibrated based on experimental results.

[0037]

[0044] Steps 302 and 304 may be repeated one or more times to collect a greater amount of data to use in creating the model. The model may be a cascade transient model that can relate position error to multiple sensor readings. The transient response of each component is determined by the component's heat capacity or thermal mass and heat transfer characteristics. This cascade empirical model may then be used to represent the thermal effects of the system.

[0038]

[0045] The model is formed using the following variables or parameters: the location (x,y) where the position reading should be during the stabilization period without thermal effects, the location (x',y') where the position reading actually is due to thermal effects, an approximation of the perturbation of the position reading (Δx,Δy) (i.e., the difference between the position reading without thermal effects and the position reading with thermal effects), the initial temperature (T), and the temperature change (ΔT) from the initial temperature reading. In one embodiment, at least the initial temperature (T) and the temperature change (ΔT) from the initial temperature reading must be known to form the model. Equations 1-4 can be used to approximate the location where the position reading actually is due to thermal effects. TIFF2025165943000002.tif39170

[0039]

[0046] In Equations 3 and 4, Φ is the spatial mode and α is the model transformation between temperature and global position change. The position perturbation (Δx, Δy) is formulated as a function of all temperature sensor readings, including both previous and current readings.

[0040]

[0047] In step 308, the model is calibrated. Calibrating the model may include continuously operating the photolithography system to model a stabilization period and keeping the photolithography system idle after the stabilization period to model a cool-down period. The model is further calibrated by formulating an optimization problem. The optimization problem is formulated to obtain model parameters to minimize an overall cost function (C) (shown in Equation 7 below). The cost is defined as the sum of the differences between measurements and model-predicted values ​​at multiple locations (x,y) and multiple temperature conditions to represent the transitions. The optimization problem can be formulated to minimize the cost function.

[0041]

[0048] An optimization problem is formulated to determine multiple heat capacities and transfer rates for the system during a stabilization period. The input to the optimizer is the collected temperature readings and corresponding position errors at multiple locations. The output of the optimizer is a set of heat capacities and transfer rates. The optimizer can minimize the difference between the measured positions on the substrate and the model-estimated positions. The model can be calibrated using Equations 5-7. Equations 5 and 6 are used for the model's prediction error, where x' meas and y' meas is the measured position change due to thermal effects. Equation 7 is the cost function, where K is the number of collected data and L is the number of calibrated parameters. TIFF2025165943000003.tif36170

[0042]

[0049] In step 310, the calibrated model is used to estimate errors during a subsequent stabilization period, and the estimated errors during the subsequent stabilization period are dynamically corrected. After the model is calibrated, it can be used to correct predicted position errors and perturbations due to thermal effects during the subsequent stabilization period. As noted above, thermal effects are just one type of effect or variation that can be considered and are not intended as a limiting example. The estimated position errors during the stabilization period can be corrected by dynamically modifying the digital mask of the photolithography system on the fly, rather than by corrections or changes made to the physical photolithography system itself. The correction of the estimated position errors can be dynamic digital corrections applied plate-by-plate or substrate-by-substrate during exposure to the digital mask.

[0043]

[0050] The calibrated model can be further used to monitor the stability of the photolithography system. A registration model that models the alignment of the digital mask can be formed based on the calibrated model. The registration model can then be compared to the registration of the digital mask during subsequent stabilization periods. The comparison of the registration during subsequent stabilization periods with the registration model can be used to determine a similarity metric. The similarity metric can be used to determine whether subsequent stabilization periods are the same as the initial stabilization period used to create the model (i.e., whether the same position perturbations occur in the subsequent stabilization periods as in the initial stabilization period). The similarity metric can determine the stability of the system by determining whether the same position perturbations occur repeatedly. If the system is stable, the same errors will occur repeatedly at the same time points during the stabilization periods, facilitating estimation of potential position errors.

[0044]

[0051] In at least one embodiment, the model can be a machine learning model or problem with model guidance, such as a neural network. For example, if a large amount of data is available, the photolithography system can use multiple sensors and the large amount of data to proactively correct for frequently occurring positional perturbations or errors before the errors occur during a subsequent stabilization period. The system can use the data, sensors, and / or model to estimate or determine frequently occurring errors and compensate for potential errors before they occur. After compensating for frequently occurring perturbations or errors, the system can further compare the current printing position with the model to determine whether the compensation actually corrected the potential errors and can make additional adjustments if necessary. Thus, instead of correcting errors on the fly as they occur, the system can use machine learning algorithms to proactively compensate for potential errors before they occur.

[0045]

[0052] Using the above methods, the behavior of a photolithography system can be accurately modeled and calibrated to estimate position perturbations that occur during stabilization periods, thereby improving the repeatability of total pitch and overlay corrections. The model can then be used to correct overlay and total pitch errors on the fly during the system's subsequent stabilization period by adjusting the digital mask. Furthermore, if a large amount of data is available to the system, the system can use machine learning models with model guidance to proactively compensate for potential position perturbations or errors before they occur.

[0046]

[0053] By utilizing this model for dynamic position correction, costly hardware solutions can be eliminated or reduced. Because the position correction is applied to the digital mask, the model can be easily used for dynamic position correction. Furthermore, because the model is a software-based solution, new model formats can be developed to include new effects not previously included or covered, or to include additional sensors not originally available. In this way, photolithography systems can be accurately utilized for plate or substrate exposure during their stabilization period.

[0047]

[0054] While the forgoing is directed to embodiments of the present disclosure, other and further embodiments of the present disclosure may be devised without departing from the basic scope thereof, which scope is determined by the following claims.

Claims

1. Starting the photolithography system and entering a stabilization period; collecting data and position readings while the photolithography system is printing during the stabilization period; creating a model based on the data and the position readings; using said model to dynamically correct estimation errors during a subsequent stabilization period; A method comprising:

2. The method of claim 1 , wherein the data collected is temperature data, and wherein the temperature data is collected using multiple temperature sensors located throughout the photolithography system.

3. 3. The method of claim 2, wherein the model is formed using one or more parameters selected from the following group: where the position reading should be during the stabilization period without thermal effects, where the position reading actually is due to thermal effects, an approximation of a perturbation in the position reading, an initial temperature of the photolithography system, and a measured temperature change from the initial temperature after a predetermined time has elapsed.

4. The method of claim 2 , wherein the temperature data is collected during a heating period and a cooling period of the stabilization period.

5. The method of claim 1 , wherein the collected data is pressure data or the collected data is humidity data.

6. The method of claim 1 , wherein the models are a set of cascaded transient models.

7. Starting the photolithography system and entering a stabilization period; collecting temperature data and position readings while the photolithography system is printing during the stabilization period, the temperature data being collected during a heating period and a cooling period; creating a model based on the temperature data and the position readings; calibrating the model; using the calibrated model to estimate the error in a subsequent stabilization period; dynamically correcting the estimated error during the subsequent stabilization period; A method comprising:

8. The method of claim 7 , wherein pressure data is further collected and the model is developed based on the pressure data, or humidity data is further collected and the model is developed based on the humidity data.

9. The method of claim 7 , wherein the models are a set of cascaded transient models.

10. The method of claim 7 , wherein the temperature data is collected using multiple temperature sensors located throughout the photolithography system.

11. 8. The method of claim 7, wherein the model is formed using one or more parameters selected from the following group: where the position reading should be during the stabilization period without thermal effects, where the position reading actually is due to thermal effects, an approximation of a perturbation in the position reading, an initial temperature of the photolithography system, and a measured temperature change from the initial temperature after a predetermined time has elapsed.

12. Starting the photolithography system and entering a stabilization period; collecting temperature data and position readings while the photolithography system is printing during the stabilization period; creating a model based on the temperature data and the position readings; Formulating an optimization problem to determine the heat capacity and transfer rate of the photolithography system; using the model and the optimization problem to estimate the error in a subsequent stabilization period; and dynamically correcting the estimated error during the subsequent stabilization period; A method comprising:

13. The method of claim 12 , wherein pressure data is further collected and the model is developed based on the pressure data, or humidity data is further collected and the model is developed based on the humidity data.

14. The method of claim 12 , wherein the temperature data is collected during a heating period and a cooling period of the stabilization period, and the model is a set of cascade transient models.

15. 13. The method of claim 12, wherein the model is formed using one or more parameters selected from the following group: where the position reading should be during the stabilization period without thermal effects, where the position reading actually is due to thermal effects, an approximation of a perturbation in the position reading, an initial temperature of the photolithography system, and a measured temperature change from the initial temperature after a predetermined time has elapsed.

Images